Unifying Decision-Making and Trajectory-Planning in Unsignalized Intersections Using Time-Varying Potential Fields
- URL: http://arxiv.org/abs/2607.13626v2
- Date: Wed, 22 Jul 2026 10:59:02 GMT
- Title: Unifying Decision-Making and Trajectory-Planning in Unsignalized Intersections Using Time-Varying Potential Fields
- Abstract summary: This paper presents a novel framework for integrated Decision-Making (DM) and Trajectory Planning (TP) for automated vehicles at unsignalized intersections.<n>The proposed method effectively unifies DM and TP, ensuring the generation of a feasible and safe reference trajectory.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a novel framework for integrated Decision-Making (DM) and Trajectory Planning (TP) for automated vehicles at unsignalized intersections. The approach leverages a Finite Horizon Optimal Control Problem (FHOCP) that employs Time-Varying Artificial Potential Fields (TV-APF). By utilizing short-horizon motion prediction and a dedicated conflict-zone occupancy coefficient, the framework suitably accounts for potential collisions within the FHOCP. The proposed method effectively unifies DM and TP, ensuring the generation of a feasible and safe reference trajectory. Simulation results in multi-vehicle traffic scenarios demonstrate the effectiveness of the approach.
Related papers
- Multimodal Trajectory Planning for Surface Vehicles using Turning Circle-based Control Barrier Functions [0.0]
The proposed method integrates model predictive control (MPC) with a turning circle-based control barrier function (TC-CBF)<n>The TC-CBF accounts for the nonholonomic motion and finite turning capability of a surface vehicle.<n>Its geometric formulation identifies feasible avoidance regions according to the vehicle's turning circles and generates distinct left- and right-turning avoidance modes.
arXiv Detail & Related papers (2026-08-20T01:18:07Z) - Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment [1.6822770693792821]
radial basis function network (RBFN)-informed motion planning framework for safe and efficient urban autonomous driving.<n>Network learns jerk-minimal trajectories, enabling the MPC to operate within a reduced and dynamically consistent search space.<n>Results demonstrate improved risk awareness and fewer vehicle-limit violations compared to benchmark methods.
arXiv Detail & Related papers (2026-07-29T11:41:00Z) - Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats [76.98547580312116]
This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs)<n>To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent avoidance.
arXiv Detail & Related papers (2026-07-22T16:37:05Z) - Proposal-Conditioned Latent Diffusion for Closed-Loop Traffic Scenario Generation [1.6711881128691604]
We present a diffusion-based scenario generation framework conditioned on instance-centric scene context and multimodal proposal priors.<n>Experiments on the Open Motion dataset demonstrate a favorable balance among realism, safety, and controllability across diverse interactive scenarios.
arXiv Detail & Related papers (2026-06-25T15:01:31Z) - JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems [1.9236465591431287]
This paper presents a joint prediction-planning diffusion framework that treats participant prediction and robot planning as a single conditional trajectory generation problem.<n> Experiments in scenario-grounded simulation, naturalistic pedestrian replay, Isaac Sim validation, and ROS/Orin deployment show that joint sampling improves tail safety and runtime efficiency.
arXiv Detail & Related papers (2026-06-14T11:15:44Z) - Bridging Predictive Uncertainty and Safe Action: Sample-Conditioned Differentiable Planning for Autonomous Driving [46.91409391705346]
Complex, dynamic, and interactive driving environments pose significant challenges for autonomous driving.<n>A fundamental bottleneck in current systems is the disconnect between highly expressive uncertainty modeling and interpretable, safe motion planning.<n>We propose a novel sample-conditioned differentiable planning framework that bridges this gap by explicitly incorporating diffusion-generated future trajectories into the optimization process.
arXiv Detail & Related papers (2026-06-02T08:10:02Z) - Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets [6.0967385124149756]
Agile planning using existing non-linear model predictive control methods is limited by the number of planning steps as it becomes increasingly demanding.<n>In this paper, we propose to address these limitations by introducing variable time steps and coupling them with the prediction horizon length.<n>A simplified point-mass motion primitive is used to leverage the differential flatness of quadrotor dynamics and the trajectory generation of feasible trajectories in the flat output space.
arXiv Detail & Related papers (2025-03-18T11:59:24Z) - Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models [57.45019514036948]
Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics.<n>This work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces.
arXiv Detail & Related papers (2024-12-23T21:27:19Z) - Traj-MAE: Masked Autoencoders for Trajectory Prediction [69.7885837428344]
Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers.
We propose an efficient masked autoencoder for trajectory prediction (Traj-MAE) that better represents the complicated behaviors of agents in the driving environment.
Our experimental results in both multi-agent and single-agent settings demonstrate that Traj-MAE achieves competitive results with state-of-the-art methods.
arXiv Detail & Related papers (2023-03-12T16:23:27Z) - AI-aided Traffic Control Scheme for M2M Communications in the Internet
of Vehicles [61.21359293642559]
The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies.
We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
arXiv Detail & Related papers (2022-03-05T10:54:05Z) - Federated Learning on the Road: Autonomous Controller Design for
Connected and Autonomous Vehicles [109.71532364079711]
A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs)
A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs.
A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
arXiv Detail & Related papers (2021-02-05T19:57:47Z) - Multimodal Trajectory Prediction via Topological Invariance for
Navigation at Uncontrolled Intersections [45.508973373913946]
We focus on decentralized navigation among multiple non-communicating rational agents at street intersections without traffic signs or signals.
Our key insight is that the geometric structure of the intersection and the incentive of agents to move efficiently and avoid collisions (rationality) reduces the space of likely behaviors.
We design Multiple Topologies Prediction (MTP), a data-driven trajectory-prediction mechanism that reconstructs trajectory representations of high-likelihood modes in multiagent intersection scenes.
arXiv Detail & Related papers (2020-11-08T02:56:42Z) - The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
Roads have well defined geometries, topologies, and traffic rules.
In this paper we propose to incorporate structured priors as a loss function.
We demonstrate the effectiveness of our approach on real-world self-driving datasets.
arXiv Detail & Related papers (2020-06-04T03:56:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.